Identification and validation of Alzheimer’s disease-related metabolic brain pattern in biomarker confirmed Alzheimer’s dementia patients

Metabolic brain biomarkers have been incorporated in various diagnostic guidelines of neurodegenerative diseases, recently. To improve their diagnostic accuracy a biologically and clinically homogeneous sample is needed for their identification. Alzheimer’s disease-related pattern (ADRP) has been identified previously in cohorts of clinically diagnosed patients with dementia due to Alzheimer’s disease (AD), meaning that its diagnostic accuracy might have been reduced due to common clinical misdiagnosis. In our study, we aimed to identify ADRP in a cohort of AD patients with CSF confirmed diagnosis, validate it in large out-of-sample cohorts and explore its relationship with patients’ clinical status. For identification we analyzed 2-[18F]FDG PET brain scans of 20 AD patients and 20 normal controls (NCs). For validation, 2-[18F]FDG PET scans from 261 individuals with AD, behavioral variant of frontotemporal dementia, mild cognitive impairment and NC were analyzed. We identified an ADRP that is characterized by relatively reduced metabolic activity in temporoparietal cortices, posterior cingulate and precuneus which co-varied with relatively increased metabolic activity in the cerebellum. ADRP expression significantly differentiated AD from NC (AUC = 0.95) and other dementia types (AUC = 0.76–0.85) and its expression correlated with clinical measures of global cognition and neuropsychological indices in all cohorts.

www.nature.com/scientificreports/ Alzheimer's disease-related pattern (ADRP) has been identified previously in four different cohorts of patients with clinically diagnosed Alzheimer's dementia (AD) 12,[14][15][16] . However, clinical diagnosis is not in concordance with pathological findings in around 30% of AD cases 17 and therefore clinically defined cohorts may be heterogeneous in their underlying cause of dementia 18 . Consequentially previously identified ADRPs may not be specific enough. Cerebrospinal fluid (CSF) biomarkers closely reflect Alzheimer's pathology [19][20][21] .
The aims of this study were to (i) identify ADRP in a cohort of CSF biomarker-positive AD patients; (ii) to correlate the newly identified ADRP expression with patients' clinical measures; (iii) to validate it on independent cohorts of patients with AD, behavioral variant of frontotemporal dementia (bvFTD) and on two mild cognitive impairment (MCI) cohorts, one due to Alzheimer's disease and one due to other causes.

Methods
Participants. 301 subjects from three different cohorts were included in the study. To identify and internally validate ADRP, we included 63 patients who fulfilled diagnostic criteria for AD (amnestic presentation 22 ), had Alzheimer (Alz) biomarker CSF profile, i.e. A+/T+/N+ or A+/T+/N− 2,22 and underwent 2-[ 18 F]FDG PET brain imaging. Additionally, we included 42 patients with MCI who had available CSF information and 2-[ 18 F]FDG PET brain scans. Patients with MCI were cognitively tested by neuropsychologist, n = 19/42 23 , or by neurologist using MoCA test, n = 23/42 24,25 . We also included 15 patients with probable bvFTD with diagnosis confirmed at a follow-up visit at least 18 months after 2-[ 18 F]FDG PET scanning 26 and 41 normal controls (NCs) scanned with 2-[ 18 F]FDG PET for purposes of another research project 27 . We excluded patients with structural brain lesions (e.g. tumor, stroke) or systemic condition (e.g. hypothyroidism, B 12 deficiency) that could cause or significantly contribute to cognitive impairment. All patients and NC from identification and internal validation cohorts underwent 2-[ 18 F]FDG PET brain imaging between January 2010 and April 2019 at the University Medical Center Ljubljana (UMCL), Slovenia.
For external validation, we randomly selected 60 AD patients with Alz CSF profile and 60 NC with normal (A−/T−/N−) CSF profile from Alzheimer's disease neuroimaging initiative (ADNI) database. Further, we also analyzed a previously described cohort of 10 patients with clinically diagnosed AD (AD-NS) 28 , and 10 agematched NC (NC-NS) 12 from North Shore University Hospital, Manhasset, New York, USA.
Internal validation groups. To validate the newly-identified ADRP, we analyzed data from the remaining 43 AD patients with Alz CSF profile and additional 15 bvFTD patients, 42 patients with MCI (27 with Alz CSF profile and 15 with normal or non-Alz CSF profile), and 21 NC subjects (NC2). Patients underwent clinical neurological and neuropsychological examination, cognitive assessment using MMSE 30 and MoCA 24 tests, as well as structural (MRI or CT) and 2-[ 18 F]FDG PET brain imaging.
External validation groups. To externally validate ADRP, we analyzed data from ADNI database (60 AD and 60 NC) and previously described cohort from North Shore University Hospital (10 AD and 10 NC) 12 .
The ADNI (https:// adni. loni. usc. edu) was launched in 2003 as a public-private partnership, led by Principal Investigator Michael W. Weiner, MD. The primary goal of ADNI has been to test whether serial MRI, PET, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early Alzheimer's disease.
We selected an age homogenous group of 60 AD patients 28 with Alz CSF profile 31,32 and 60 NC with normal CSF profile, using an in-house script. First, we randomly picked the groups, then the script was used to randomly replace individuals until the standard deviation of age did not change after 10,000 iterations. Neuropsychological assessment. 30 34 . Despite the limitation of missing MRI data, the goal of these analyses was to examine that the ADRP identification and validation groups did not differ in hippocampal atrophy and vascular burden at a group level. In patients with inaccessible MRI, we checked CT scans obtained with Siemens Biograph mCT PET/CT scanner for structural changes.  9,13 . The number of 20 diseased and healthy have been shown optimal in previous studies 15,35 . Further analysis was limited to principal components (PCs) that each accounted for at least 5% of the total variance (VAF). Subject scores for these PCs were then entered into a series of logistic regression models, with group as dependent and subject scores as the independent variable. The model with the lowest Akaike Information Criterion score was selected as the ADRP. Estimated disease-related metabolic pattern was tested for reliability using bootstrap resampling with 1000 iterations 36 . Pattern stability was assessed also with threefold cross-validation procedure using the data from internal validation group (AD2 and NC2). For the calculation of pattern expression, i.e. subject score, in 2-[ 18 F]FDG PET images from subjects not included in the identification cohort topographic profile rating (TPR) was used 9 . In TPR, logarithmically transformed and double centered subject vectors were multiplied by the ADRP. Raw scores were Z-transformed based on the mean pattern expression and standard deviation of subject scores in the NC1 group.
Statistical analysis. Data distribution was tested for normality using Shapiro-Wilk test. Student's independent-sample t-test or one-way analysis of variance (ANOVA) with post hoc Tukey HSD test was used to examine differences in age, MMSE, MoCA, disease duration, MTA scores and Fazekas scores in the NC and patient groups. Fisher's Exact Test for Count Data was used to examine differences in sex distribution. For the non-normally distributed data, the non-parametric tests (e.g. Wilcoxon-rank sum test, Spearman's rank correlation and Kruskal Wallis test with post hoc Dunn's test) were additionally performed to examine whether the significant results and statistical inferences of the corresponding parametric tests were changed. To examine differences between normalized ADRP subject scores in the pattern identification group (AD1 and NC1), we used a robust exact Fisher-Pitman permutation test, which is data-dependent and free of assumptions of underlying distribution 37 . Correlations between ADRP expression and MMSE, MoCA, disease duration and neuropsychological scores were evaluated with Pearson correlation analysis. One-way ANOVA with post hoc Tukey HSD test was used to examine differences in ADRP expression in two internal dementia groups (AD2, FTD) and NC2 and between MCI Alz, MCI nonAlz and NC2 groups. We used Student's independent-sample t-test to examine the differences in pattern expression scores between NC1 and NC2, between AD1 and AD2 and when comparing AD1 and AD2 to MCI Alz groups and external dementia validation groups from ADNI and NS. We used pROC package to calculate area under the curve (AUC), specificity and sensitivity based on the optimal cut-point determined by Youden's index for the internal validation group (AD2 vs NC2) 38 . All statistical analyses were performed in RStudio version 1.3.1093, R version 3.6.0 (R Foundation for Statistical Computing, Vienna, Austria) and results were considered significant at p < 0.05 (two-tailed).

Ethics approval and consent to participate. The study was approved by Slovenian National Ethics
Committee (0120-584/2019/5) and institutional review boards of collaborating institutions. All patients gave informed consent. The study was designed and conducted in accordance with the relevant guidelines and regulations of the ethical principles for medical research involving human subjects. ADRP identification. AD1 and NC1 participants did not differ in mean age (p = 0.44) or sex distribution (p = 0.09). AD1 had significantly lower MMSE scores than NC1, p < 0.001. Four principal components: PC1 (28.7% VAF), PC2 (9.6% VAF), PC3 (6.7% VAF) and PC4 (5.4% VAF) were entered into a series of logistic regression models for further analysis. Model that incorporated PC1 alone was determined as the best to discriminate NC1 from AD1. The ADRP was characterized by relatively reduced metabolic activity in temporoparietal cortices, posterior cingulate, thalami and precuneus which co-varied with relatively increased metabolic activity in the cerebellum, Fig. 1a. Bootstrapping proved pattern stability at z =|1.96|, p < 0.05, Fig. 1b. Cross-validation procedure showed strong and significant correlations between the topography of the three patterns (see Supplementary Information). All subsequent analyses were done using ADRP obtained with data from AD1 and NC1 participants. Pattern

Internal ADRP validation.
There was a significant difference in age between the three validation groups (F(2, 76) = 9.7, p < 0.001) and no difference in sex distribution. Post hoc comparisons showed that mean age was significantly higher in AD2 compared to NC2 (p < 0.001), but no difference was found between AD2 and bvFTD (p = 0.16) or bvFTD and NC2 groups (p = 0.18).

Discussion
In this study we newly identified an Alzheimer's disease-related pattern-ADRP, a metabolic biomarker of AD. The ADRP is characterized by relatively reduced metabolic activity in temporoparietal cortices, posterior cingulate, thalami and precuneus which co-varied with relatively increased metabolic activity in the cerebellum. Cortical regions, associated with ADRP, have been shown previously to be involved in AD pathology. Although our understanding of the synergy between amyloid, tau and neurodegeneration remains incomplete 39 we know that amyloid pathology begins in neocortical regions, i.e. temporal and parietal cortices and precuneus which are also part of ADRP, and later spreads to cingulate cortex and subcortical regions 40 . In numerous regions with amyloid deposits, reduction in brain metabolism has been observed 41 . Tau pathology on the other hand starts in transentorhinal cortex and affects other cortical areas only in later stages 42 . Its close correlation with hypometabolic brain changes is well known 43 . Imaging studies using either metabolic connectivity approaches or resting-state functional MRI have identified changes in similar cortical regions as comprised in ADRP 44,45 . While amyloid and tau depositions are seen in the cerebellum only in later stages 40,46 , increased metabolic activity in cerebellum, has been observed before in AD patients 12,14,16 . Cerebellum has extensive anatomical connections with the neocortex 47 , therefore its compensatory mechanism in the context of underlying pathology was proposed 48 . ADRPs have been identified before 12,14,15 , but never in biomarker confirmed AD patients. Using a biologically heterogeneous group may have caused a lower accuracy of this biomarker in out of sample validations, AUC = 0.85-0.90 15,16 and is indeed lower than out of sample accuracy of AUC = 0.95 achieved in our study. The topography of newly identified ADRP does significantly, but moderately, correlate (r = 0.51, p < 0.0001) with previously identified ADRP 12 , which may be caused by lack of biomarker confirmed diagnoses of AD patients in previous study, but also other factors such as different scanners or the usage of different reconstruction algorithms may have had an effect on pattern topography 49 .
We validated ADRP in two ways; statistically (i.e. bootstrapping and with threefold cross validation) and by analyzing three additional independent AD patients' datasets. The pattern was stable on bootstrapping and we observed high correlations between PC1 of the three patterns. Further, we have shown that ADRP expression is significantly higher in AD patients compared to NC in various independent cohorts, which differentiates AD from NC with high specificity (90-100%) and sensitivity (80-91%). We paid attention to the possible effect of age on ADRP expression. Although AD patients were older than NC in the internal validation dataset, ADRP expression stayed significantly higher in AD patients after adjustment for age. Furthermore, ADRP expression did not correlate with age in any of the AD groups (data not shown). In addition, while the majority of our data were normally distributed, few variables in several groups (e.g., ADRP expression in the AD2 group) turned out to be non-normally distributed (p > 0.05, Shapiro-Wilk tests). Nonetheless, further analyses with equivalent www.nature.com/scientificreports/ non-parametric tests confirmed the significant results and statistical inferences of the parametric tests reported in our study. An important measure of pattern's clinical relevance is its correlation with subjects' cognitive disability. ADRP expression scores correlated well with cognitive impairment in all AD groups. We observed moderate to high correlations of ADRP expression with MMSE in the identification and validation AD datasets, but not in AD cohort from ADNI database in which ADRP expression correlated with MoCA score. We believe that this may be a consequence of rather mild dementia in ADNI cohort with an average MMSE score of 23.5 (± 2). MoCA scores had a bigger range as this test is more sensitive to subtle cognitive changes 50 . Previous studies on ADRP also reported negative correlations between MMSE and ADRP expression 12,14 .
We observed a non-significant correlation of ADRP with disease duration in all AD groups. We believe that lack of correlation between ADRP expression and disease duration, which one would anticipate to be present, may be due to the insidious disease onset and difficulty of patients and caregivers in defining disease onset. Furthermore, ADRP is a biomarker of neurodegeneration, which starts when patients are still asymptomatic 41 . Previous studies did not report on this correlation. Furthermore, we did not observe any correlation between disease duration and measurements of global cognition (r values between −0.1 and 0.37, all p > 0.37).
A subset of our patients underwent a thorough neuropsychological evaluation. In these patients ADRP expression correlated significantly with indices of several cognitive domains. Negative correlations were observed with www.nature.com/scientificreports/ immediate memory, visuospatial constructional and attention indices which is in line with previous studies 12, 14 . Both Mattis et al. 12 and Teune et al. 14 observed moderate correlations between ADRP expression and worse performance on tests of memory. We observed a negative correlation with immediate memory index, but not with delayed memory index. Absent correlation between ADRP expression and delayed memory index can be caused by the floor effect, since the majority of our patients scored below first percentile. ADRP expression did however correlate with tests of delayed memory in ADNI cohort where no floor effect was observed (r(30) = −0.54, p = 0.001). Teune et al. 14 also observed negative correlations with tests of attention, which is in line with our findings. They observed numerically similar correlations with test of visuospatial construction (r = −0.55) to our study (r = −0.58), although, in contrast to our finding, it did not reach statistical significance, which might be due to their smaller sample size (n = 11 vs. n = 15). While Mattis et al. 12 observed a negative correlation with tests of executive function and Teune et al. 14 observed non-significant correlations, the RBANS test battery, used in our study, does not contain executive function test and this correlation could not be tested in internal validation cohort. However, a significant, moderate correlation between higher ADRP expression and worse performance on Trailmaking test B was seen in ADNI cohort (r(34) = 0.51, p = 0.001). Both aforementioned studies observed significant correlations between ADRP expression and tests of language, which was not significant in our sample. This may be either due to our sample size (n = 15) or a variable cognitive presentation observed in AD patients, particularly in language domain 51 . Further studies, focusing on neuropsychological correlations, could offer additional insight into clinical correlations of ADRP.
Our results suggest that ADRP can be useful in differentiating between dementia syndromes. The pattern expression differentiated AD from bvFTD with high specificity, but limited sensitivity. Expression of ADRP has been previously studied in other neurodegenerative dementias. It was found that in comparison to NC, ADRP expression is higher in patients with dementia with Lewy bodies, Parkinson's disease dementia and bvFTD 16 . Similarly, we found a higher-than-normal expression of ADRP in a cohort of bvFTD patients, which may be due to overlapping areas of neurodegeneration in these two diseases. FTD-related pattern is characterized by hypometabolic regions that are also a part of ADRP (i.e. inferior frontal, superior temporal and thalamus) 52 .
To check the performance of ADRP in earliest stages of Alzheimer's related cognitive impairment we analyzed two groups of MCI patients: one due to Alzheimer's disease and one due to other causes. The expression of ADRP was significantly elevated in the first compared to the latter. This trend (p = 0.05) remains after adjustment for age difference (see Supplementary Material for additional information).
Limitations of our study are mostly related to its retrospective design, which enabled us to analyze a large number of scans. The time difference between lumbar puncture and 2-[ 18 F]FDG PET imaging varied between patients. We analyzed only patients with a maximum of 4 years (M = 2 months) time difference, based on previous reports on longitudinal stability of Alz CSF biomarkers at 4 years from the baseline 53,54 . Patients from different cohorts/centers were assessed by similar but not same protocols, therefore some studied subgroups are small. Furthermore, using data obtained on different scanners at different institutions could introduce unwanted data variability, which, if anything, would reduce the discriminative power of ADRP. The PET images have not been corrected for partial volume effect (PVE), which if done properly might improve ADRP performance by mitigating the regional atrophic effects. However, such analysis was beyond the scope of the current paper and the effect of PVE correction remains to be determined in future research.

Conclusions
In our study we identified ADRP in a cohort of biomarker defined AD patients which was not done before. The precise, possibly pathologically confirmed diagnosis of the identification cohort is of utmost importance particularly when deriving a biomarker. We confirmed in this study that ADRP is a robust metabolic biomarker of AD which closely correlates with patients' cognitive impairment. It could serve as a supportive diagnostic tool to clinicians and as a measure of specific AD-related neurodegeneration for research purposes. Its greater utility may be achieved in conjunction with specific metabolic biomarkers of other neurodegenerative dementias and by the application of novel analytical tools.

Data availability
The datasets generated during and analyzed during the current study are available from the corresponding author on reasonable request and signing a data-sharing agreement. The dataset used from ADNI repository is available at: https:// adni. loni. usc. edu. www.nature.com/scientificreports/